39 results about "Color constancy" patented technology

The invention relates to the technical field of underwater image enhancement, in particular to an underwater image enhancement method based on balance, correction and deblurring and application. The balance module is cascaded by color balance and illumination balance, and the color balance performs compensation balance on RGB (Red Green Blue) color channels of the underwater image based on a white balance algorithm of red channel compensation; according to illumination balance, an improved contrast-limited adaptive histogram enhancement method is used for enhancing illumination of a low-illumination area of an underwater image, and meanwhile illumination of a high-illumination area is restrained, so that illumination balance is achieved. In the correction module, a color and illumination composite correction method combining color constancy and an illumination diagram is provided so as to correct the color and illumination of the underwater image at the same time. And in the deblurring module, the image is deblurred by adopting a multi-scale anti-sharpening mask, so that the definition of the underwater image is improved.

The invention discloses a color constancy method based on physical imaging characteristics and light source spectral power embedding. According to the method, the consistency of image colors under different illumination conditions is ensured through a series of fine steps. The method comprises the following steps of: firstly, rendering spectral data by adopting a sunlight and ambient light dual-light-source model, converting the spectral data into an RGB space through a CIE standard observer function, and generating spectrums under different color temperatures and corresponding RGB data of the spectrums; then, a characteristic spectrum of a non-shadow area in the scene is extracted to serve as input of a variational auto-encoder, and hidden layer distribution of the auto-encoder guides a light source decoder to output light source spectrum power distribution matched with the label. In addition, spectral features and color and texture features of an RGB image are extracted through a convolutional neural network, and the features are fused to estimate the chromaticity of an image light source. And finally, according to the estimated chromaticity of the light source, performing color correction on the original RGB image by using a colorimetric principle, so that the image color adapts to a standard light source.

The invention discloses a color constancy method based on wavelet attention and a VMama network, and belongs to the field of computer vision and image enhancement. According to the method, illumination parameter estimation is carried out by adopting a designed WA-VMama network, and then color correction is carried out by adopting the estimated illumination parameters, so that a corrected image under standard illumination is obtained. According to the method, a designed wavelet attention mechanism is embedded into VMama, and a deep network model (WA-VMama for short) is constructed, so that global information can be effectively extracted by using the network, local information can be considered, the method has higher feature extraction and expression ability, and the accuracy of illumination parameter estimation is improved.

The invention discloses an underwater image color constancy enhancement method and system, and relates to the technical field of underwater image enhancement, and the method comprises the steps: receiving underwater image data, inputting the underwater image data into a pre-established multi-level color correction model based on an adaptive correction factor, and outputting to obtain a corrected image; inputting the corrected image into a pre-established multi-scale joint restoration network model, outputting to obtain a restored image, inputting the restored image into a pre-established residual error-based detail enhancement network model, and outputting to obtain an enhanced image; and inputting the enhanced image into a pre-established underwater image enhancement network model for optimization training, outputting and obtaining a trained underwater image enhancement network model, obtaining a to-be-tested underwater image data set, inputting the to-be-tested underwater image data set into the trained underwater image enhancement network model, and obtaining a to-be-tested underwater image data set. And an underwater image enhancement result is output, so that the color reality and naturalness of the image are remarkably improved.

The invention discloses an indoor scene-oriented color constancy algorithm error evaluation and prediction method and system, and belongs to the technical field of computers and information services. The method comprises the following steps: acquiring an indoor scene color constancy data set containing portrait content; acquiring real light source color information of the color cast image in the data set; obtaining estimated light source color information calculated based on a to-be-evaluated color constancy algorithm; performing color space conversion on the light source color information; and for a to-be-evaluated color constancy algorithm, according to the real light source color attribute, the estimated light source color attribute and the color constancy algorithm error evaluation quantification model, obtaining a corresponding estimated value, and realizing characterization of light source color estimation accuracy of different color constancy algorithms. According to the invention, high-precision and high-stability error evaluation of a color constancy algorithm for an indoor scene containing portrait content is realized.

The invention discloses a semi-supervised non-contact neonatal jaundice home intelligent early warning method based on image calibration, and the method comprises the steps: building a camera response function database, and building a color constancy depth model and a skin region segmentation network; designing a strong and weak enhancement strategy by using a large amount of label-free home data, constructing a time and context comparison learning module, and performing self-supervised training on a feature encoder; a small amount of labeled hospital data and a large amount of unlabeled family data are combined, and semi-supervised fine tuning is realized through pseudo label generation and supervised comparative learning; image sequences continuously uploaded by a user are input into the model, multi-day prediction of the bilirubin level is achieved, prediction uncertainty is quantified in combination with a Bayesian method, the risk boundary is dynamically adjusted, and personalized early warning is provided. According to the method, a user does not need to use a physical colorimetric card, color measurement errors caused by model differences of mobile equipment and variability of household illumination are inhibited from a data source, and the accuracy and robustness of subsequent jaundice assessment are improved; through a training framework combining self-supervised pre-training and semi-supervised fine tuning, the understanding ability and generalization performance of the model for the sequential characteristics of jaundice are improved.

The invention provides a method executed by electronic equipment, the electronic equipment, a storage medium and a program product. The method comprises the steps of obtaining a first image; performing illumination prediction on the first image through a first AI model to obtain a second image; wherein the first AI model is obtained by training based on training data including a sample image acquired for an object under an illumination condition, and a first real image and a second real image corresponding to the sample image; wherein the first real image is related to the illumination of the collected sample image, and the second real image is related to the color presented by the object in the sample image. The accuracy of illumination prediction can be improved on the basis of solving the problem of color constancy. Optionally, the method executed by the electronic equipment can be executed by using an artificial intelligence model.

The invention discloses an achromatic prior guidance-based unsupervised color constancy method, which relates to the field of image processing, and comprises the following steps of: designing a U-shaped coding and decoding color constancy network model, taking a network public large light source-free label data set as a training set, and inputting the training set into the network model for training; designing a space attention mechanism, and adding the model to further improve the performance; an unsupervised training method is designed, the training process does not depend on knowledge of the actual color of a light source in a scene, a training network detects achromatic pixels after a color image is converted into a gray level or a gradient, network parameters are adjusted according to a loss function gradient until the maximum number of iterations is reached, and a network model is output; and inputting a test set image with a real light source label into the trained network model, and outputting a color corrected image. According to the improved network model, the error between image light source estimation and a truth value, the visual effect of an image and the compatibility of equipment are obviously improved.

The present invention provides a color constancy method, system and storage medium based on polarization parameters, which relates to the technical fields of computer vision, image processing and color enhancement. First, a color cast image and a polarization image corresponding to the color cast image are collected, then the Stokes parameters of the polarization image are calculated, the polarization degree values corresponding to each pixel point are obtained based on the Stokes parameters, and then the effective pixel points of the color cast image are extracted through the polarization degree values. Then, the pseudo-gray pixels and gray pixels in the effective pixel points are distinguished and the gray pixel weights are obtained, and then the values of the gray pixels in the color cast image are weighted and averaged to obtain a global illumination color estimation for eliminating the illumination color of the color cast image. Finally, a colorless cast image is obtained, which can effectively eliminate the adverse effects caused by pseudo-gray pixels in the image on the illumination color estimation, and improve the accuracy and robustness of the color constancy algorithm.

The invention belongs to the technical field of color constancy, and discloses an AWB system and method for a complex light environment. Through a comparative learning module based on the illumination sensitive feature Loguv, the essential difference between single light and multiple light when the illumination condition changes is revealed by using the illumination sensitive feature Loguv, and through comparative learning, the illumination estimation effect is improved from the illumination dimension. Through a feature decoupling module and a feature reweighting module, illumination estimation is carried out by using chromaticity features and color context features at the same time. The high-efficiency lightweight shallow feature extraction module solves the problem of high-redundancy local representation when VIT-AWB captures shallow features, reduces the complexity of the model by using a depth separable technology, and further meets the lightweight demand of a multi-light color constancy network. The invention further provides an AWB method, the color constancy of the color cast image under the multi-light-source condition can be effectively achieved, and the more accurate white balance effect is achieved.

The utility model relates to a demonstration device based on a color constancy principle, and belongs to visual color science popularization education devices. The demonstration device based on the color constancy principle comprises an exhibition stand, a sliding rail device, a fixing frame, a first sliding frame and a second sliding frame. The first sliding frame slides to the rightmost end to be overlapped with the cherry picture, and the blue-green light filter only allows the blue light and the green light to pass through and filters the red light; the color actually presented by the cherries on the fused cherry picture passing through the blue-green optical filter is gray; due to the fact that visual cognition of human eyes has color constancy, when color light irradiating the surface of an object changes, people still have the invariable perceptual characteristic about the color of the surface of the object. Although the color actually presented by the cherries on the cherry picture fused by the blue-green optical filter is gray, the cherries still present red in human eyes. And when the second sliding frame slides from the rightmost end to the leftmost end, the gray cherry model is fused into the cherry picture to become red cherries. The color change of the cherry model is not the color change of the cherry model, but the color constancy principle of visual cognition of human eyes is used, and the gray cherries are recognized to be red. The color of a real object is changed in an interaction mode, audiences can truly and vividly experience human eye visual color constancy, scientificity and interestingness are increased, and the situation that the demonstration device lacks is made up.

A system, apparatus, and method for processing an image based on color constancy are provided herein. The method comprises the following steps: obtaining an image in a first color space; converting the image into data in a second color space; transforming the data using color adaptation; performing a first normalization on the transformed data, wherein the first normalization includes applying a dynamic spatial filtering technique to adjust the transformed data based on the light intensity; applying a set of filters to the normalized data, wherein the set of filters are convolved based on the normalized data associated with the image; performing a second normalization on the filtered data to obtain an illumination estimate of the image related to the filtered data; and outputting normalized data from the second normalization, where the normalized data maintains color constancy based on the illumination estimate, thereby removing illumination from the normalized data.

This invention discloses a method for detecting the freshness of giant salamander meat based on color constancy and integrated intelligent vision, belonging to the fields of food safety and computer vision technology. The method includes: acquiring RAW images of the meat sample and an indicator film and resolving them into linear radiosity tensors; constructing a color correction model using a latent diffusion local redraw network and a conditional control network; synthesizing a structure-constrained virtual reference color card in a masked region; extracting ambient light source features and performing adaptive white balance compensation to output a standard color image; inputting this image into a deep network optimized by ArcFace to extract a high-dimensional feature vector; and finally inputting this vector into an XGBoost model to fit a nonlinear decision boundary and outputting a freshness rating. This invention effectively reduces color shift interference from complex light sources, improves the recognition accuracy of samples in the critical period of color change, and provides a highly robust solution for non-destructive monitoring of giant salamander meat freshness in actual market environments.

The application discloses an indoor scene-oriented color constancy algorithm error evaluation prediction method and system, and belongs to the technical field of computer and information service. The application comprises the following steps: acquiring an indoor scene color constancy dataset containing portrait content; acquiring real light source color information of a color cast image in the dataset; acquiring estimated light source color information obtained based on a color constancy algorithm to be evaluated; performing color space conversion on the light source color information; for the color constancy algorithm to be evaluated, corresponding estimated values are obtained according to the real light source color attribute, the estimated light source color attribute and a color constancy algorithm error evaluation quantitative model, so that the light source color estimation accuracy of different color constancy algorithms is represented. The application realizes high-precision and high-stability error evaluation of the color constancy algorithm for the indoor scene containing the portrait content.

Embodiments of this disclosure can provide a system and method for white balancing images. During operation, the system can obtain labeled red, green, and blue (RGB) image samples captured by a plurality of cameras and generate a plurality of training tasks. A respective training task is associated with RGB image samples captured by a corresponding camera. The system can perform meta-training over the plurality of training tasks to obtain a meta model, with parameters of the meta model optimized based on a global loss function. The system can obtain an image captured by a first camera, fine-tune the meta model using labeled RGB image samples captured by the first camera to obtain a fine-tuned model specific to the first camera, and implement the fine-tuned model to white balance the image.

The present invention relates to a low - illumination image enhancement method with adjustable brightness based on pure noise training. Its technical features are as follows: During the training stage, random Gaussian noise is sampled as the low - illumination input image; the low - illumination input image and the brightness change factor are respectively sent into the image processing module and the brightness processing module to obtain the feature map and the feature vector; through operations of convolution and non - linear transformation on the feature map and the feature vector, the fusion and reconstruction of features are realized, and curve parameters with 6 output channels are output; the low - illumination input image is enhanced into an output image with a specific brightness guided by the amplitude of the brightness change factor; mean and contrast transformations are performed to obtain a pseudo - reference image; the total loss is calculated, and the network is iteratively optimized. The present invention adopts a pure - noise training strategy, which helps the model bypass the commonly used color constancy and illumination smoothing losses, simplifies the difficult - to - design non - linear curve form into a linear curve form, and realizes the function of cost reduction and efficiency improvement in the field of low - illumination enhancement.

The invention relates to the technical field of image processing, and particularly provides an image management method, system and device for removing illumination influence, and a storage medium, and the method comprises the steps: carrying out the color correction of a to-be-processed image through a color constancy convolution algorithm, and obtaining a standard image; extracting a feature value of the standard image by using a deep learning neural network model; calculating the similarity between the feature value and a feature value of an image stored in a database; if the maximum similarity reaches a set threshold value, it is judged that an image matched with the to-be-processed image exists in the database; and if the maximum similarity does not reach a set threshold value, storing the characteristic value of the standard image, and storing the to-be-processed image to the database. The repeated images can be accurately recognized, and the repeated images are effectively prevented from occupying a large number of storage resources.

The present invention discloses a generative adversarial network training method, an image brightness enhancement method, a computer device and a storage medium. The generative adversarial network training method includes the steps of obtaining a training set and executing a training process. The image brightness enhancement method in this embodiment utilizes the characteristics of image color constancy, utilizes the property that the same object has the same reflection ability to light of different wavelengths, constrains the consistency of the reflection components of the same group of low-brightness images and the reflection components of high-brightness images, uses a discriminator to learn the mapping of incident components of different brightness, thereby mapping the incident components of the low-brightness image to the incident components of the high-brightness image, and then combining them with the low-brightness reflection components to obtain the final calibrated high-brightness image. The image brightness enhancement method in this embodiment uses very few prior assumptions, so it can effectively avoid image deviations caused by prior assumption estimation deviations. The present invention is widely used in the field of image processing technology.

The invention discloses a vertical plastic laying machine for saline-alkali soil sealing and a plastic laying monitoring method based on machine vision, and belongs to the technical field of salt isolating membrane laying. The vertical plastic laying machine for saline-alkali soil sealing comprises a chain type furrow opener, a membrane placing mechanism and an industrial camera; the membrane placing mechanism realizes smooth laying of the isolating membrane through a membrane releasing roller, a guide roller, a vertical roller and a guide plate, and an industrial camera monitors in real time. According to the invention, isolation membrane laying and real-time quality management and control can be realized, and the operation efficiency and the treatment effect are improved. The plastic laying monitoring method based on machine vision comprises the steps that an isolating membrane image in the laying process is collected, and a corrected and enhanced image is obtained through self-adaptive illumination elimination processing; extracting illumination invariant features fusing multi-channel color constancy and gradient magnitude; a multi-scale residual error is calculated, and a defect sensitive feature graph is generated through morphological topological weight modulation; and in combination with real-time tension fluctuation, four states of normality, wrinkling, hemming and damage are identified.

The application relates to a convolutional neural network-based sequence image color constancy calculation method, which comprises the following steps: reading an original image sequence by using a preset composite model, performing image correction and cutting on the original image sequence to obtain a to-be-processed image sequence meeting preset image standard conditions; performing preset transformation processing on key frame images of the to-be-processed image sequence to obtain a simulation image sequence; extracting a first image feature sequence from the original image sequence and a second image feature sequence from the simulation image sequence by using two preset convolutional neural networks; processing the first image feature sequence and the second image feature sequence by using a gated recurrent unit (GRU) to output a first tensor and a second tensor; and generating three channels corresponding to an RGB color space based on a splicing tensor of the first tensor and the second tensor, so as to generate light source information for correcting image colors according to the three-channel features.

The invention discloses an industrial camera color constancy correction device system based on image cluster block expansion convolution. The industrial camera color constancy correction device system comprises a PC end module and a camera end module. The PC end module completes training and optimization of an illumination model, and the camera end module realizes real-time reasoning and white balance correction through an embedded hardware FPGA (Field Programmable Gate Array); the construction method for generating the lightweight illumination estimation training model by the PC end module specifically comprises the following steps: step 1, expanding a data set; 2, constructing a sectional type multi-scale expansion convolutional network model; step 3, training the expanded data set obtained in the step 1 by using the segmented multi-scale expansion convolutional network model constructed in the step 2, and iteratively training parameters; color constancy data analysis dimensions are added, image data are analyzed from two dimensions of expanding a data set and constructing a lightweight illumination estimation training model, the average angle error in an industrial scene is reduced to 1.52 degrees, the frame rate is larger than or equal to 30 fps, power consumption is smaller than 2 W, and the method has the characteristics of high precision and low delay.

The invention discloses a multi-light-source color constancy method and system based on color decoupling, multi-light-source color constancy is realized by using a pixel-level multi-light-source color constancy model, and the system comprises a content color perception module CCAM and a contrast coordination module CHAM. CCAM learns scene content color feature priori, scene content colors and illumination colors are separated by providing color features of the content for the model, and light source prediction outside the color gamut is reduced. CHAM uses spatial correlation to make the model more sensitive to the relationship between adjacent features, and uses illumination parallax to guide feature classification; the local edge contrast is improved by enhancing the uniqueness of homogeneous illumination features and the distinctiveness of heterogeneous illumination features; the soft discrimination of similar illumination features is emphasized by distributing fine-grained edge coefficients, and the local contrast is further enhanced. The high-precision multi-light-source color constancy is realized by introducing the advanced semantic information to assist the model in understanding the scene and optimizing the feature representation through the fine-grained feature constraint.

The invention discloses an outdoor scene-oriented color constancy algorithm error evaluation and prediction method and system, and belongs to the technical field of computers and information services. The method comprises the following steps: acquiring an outdoor scene color constancy data set of natural daylight illumination; acquiring real light source color information of a color cast image in the color constancy data set; obtaining estimated light source color information calculated based on a to-be-evaluated color constancy algorithm; performing color space conversion on the light source color information; and for a to-be-evaluated color constancy algorithm, according to the real light source color attribute, the estimated light source color attribute and the color constancy algorithm error evaluation quantification model, obtaining a corresponding estimated value, and realizing characterization of light source color estimation accuracy of different color constancy algorithms. According to the invention, high-precision and high-stability error evaluation of a color constancy algorithm for an outdoor scene with natural daylight illumination is realized.

This invention relates to an image color correction method, system, and electronic device based on a dual-discriminator network. To address the limitations imposed on the learning capabilities of GANs in color constancy tasks by issues such as color feature misleading and the low sensitivity of the discriminator to color information, we propose a Dual-Discriminator Generative Adversarial Network (DDGAN). It comprises a basic generative adversarial network and a Feature Fusion Discriminator (FFD) module. The FFD is a discriminator module with two feature extraction branches: one extracts color features, and the other extracts globally relevant features. The FFD fuses these features together to weaken structural features and enhance the discriminator's sensitivity to color features. Finally, to obtain a more uniform color and content image, a global consistency constraint is applied to the structural features weakened by the FFD, unifying the structural and color features, ultimately resulting in a high-precision image color correction method.

The invention discloses a video monitoring image management method and device, equipment and a medium, and relates to the technical field of image management, and the method comprises the steps: obtaining a video monitoring image through a target video camera; performing white balance processing by using a preset color constancy algorithm to generate a white balance image of the video monitoring image; vectorizing the white balance image based on a preset feature network to extract an image feature value, and judging whether a target image vector exists in a preset vector database by using a preset nearest neighbor search algorithm; and if the video monitoring image does not exist, storing the image characteristic value in a preset vector database, storing the video monitoring image in a preset image database, and constructing a vector index so as to establish a unique mapping relationship between the image characteristic value in the preset vector database and the video monitoring image in the preset image database through the vector index. Therefore, the management method of the video monitoring image can be optimized to eliminate the illumination influence and reduce the false drop rate of the image.

The invention belongs to the technical field of color constancy in computer vision, and discloses a multi-light AWB system and method based on local affine transformation. By designing a multi-spectral image assisted RGB illumination estimation clue acquisition module, more illumination information is extracted from an RGB image and a multi-spectral image to realize more accurate illumination estimation, color correction for the RGB image is realized by using color context information in the RGB image, and the process of converting from a multi-spectral domain to an RGB domain is avoided. A multi-scale model is adopted to learn joint distribution of chromaticity and color context features through a multi-scale fusion module, an affine bilateral grid is predicted based on the joint distribution, and white balance without alignment semantic representation is carried out on an RGB image by using illumination color information predicted by a multi-spectral image. The invention further provides a multi-light AWB method, the color constancy of the color cast image under the multi-light-source condition can be effectively achieved, and the more accurate white balance effect is achieved.

In order to restore the real color of the object in the image in the human eye due to the illumination color of different ambient light changing the color of the object in the image scene, the application discloses a color constancy algorithm based on gray surface extraction, which extracts the gray pixels in the image based on the illumination invariant vector, then uses the gray world algorithm on the pixels to predict the light source in the image, and corrects the color adaptation of the image. According to the color adaptation result, the gray pixels in the image are continuously detected and the color adaptation is carried out through iterative circulation until the set threshold is met, and the algorithm is stopped. Compared with the traditional algorithm, the illumination estimation accuracy is greatly improved.

This invention discloses a multi-source color constancy method and system based on color decoupling. It utilizes a pixel-level multi-source color constancy model to achieve multi-source color constancy, including a Content Color Awareness (CCAM) module and a Contrast Coordination (CHAM) module. CCAM learns prior color features of scene content, separating scene content color from illumination color by providing the model with these color features, thus reducing the prediction of out-of-gamut light sources. CHAM leverages spatial correlation to make the model more sensitive to the relationships between adjacent features and uses illumination disparity to guide feature classification; it improves local edge contrast by enhancing the uniqueness of homogeneous illumination features and the distinguishability of heterogeneous illumination features; and it further enhances local contrast by assigning fine-grained edge coefficients to emphasize the soft distinguishability of similar illumination features. This invention achieves high-precision multi-source color constancy by introducing advanced semantic information to assist the model in understanding the scene and optimizing feature representation through fine-grained feature constraints.

A cataract classification method based on color invariance and frequency domain attention belongs to the technical field of computer vision and medical artificial intelligence, takes an eye image photographed by a slit lamp as an input data source, constructs a CFC-Mamba classification network integrating a color invariance fusion module and a frequency domain channel attention module, so that the model can effectively extract color robust features which are invariant to devices and enhance the high-frequency detail perception ability to the cataract lesion boundary, adopts color invariance convolution and cross attention fusion mechanism to solve the color offset problem caused by different imaging devices and ensure the cross-device robustness of feature extraction, and through the frequency domain channel attention module, combines Fourier transform, high-pass filtering and double-path channel attention strategy to highlight the high-frequency features of the lesion boundary and improve the fine-grained feature expression ability, and provides a cataract classification system based on color invariance and frequency domain attention. The present application realizes high-precision classification of multi-granularity cataracts.

The invention provides an endoscope image enhancement method and endoscope image enhancement equipment, and relates to the technical field of image processing. According to the invention, the adaptive color constancy loss function calculated based on the first brightness adaptive weight map in the Lab color space is designed, and the adaptive exposure control loss function calculated based on the second brightness adaptive weight map is designed; the image enhancement network is trained based on the self-adaptive color constancy loss function and the self-adaptive exposure control loss function, so that the trained image enhancement network provided by the embodiment of the invention can be based on the to-be-enhanced endoscope image; adaptive learning is carried out to generate a multi-channel curve parameter graph containing a group of pixel-level brightness enhancement curve parameters, and the group of pixel-level brightness enhancement curve parameters corresponding to each pixel point in the multi-channel curve parameter graph is applied to the endoscope image to be enhanced. And the brightness contrast level is maintained while the color fidelity of the enhanced endoscope image to be enhanced is improved.